Method and system of data modelling
First Claim
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1. A method for modelling data based on a dataset, comprising:
- in a computing system, executing;
a training phase, wherein the dataset is applied to a non-stationary Gaussian process kernel in order to optimize the values of a set of hyperparameters associated with the non-stationary Gaussian process kernel, andan evaluation phase in which the dataset and the non-stationary Gaussian process kernel with optimized hyperparameters are used to generate model data, wherein the evaluation phase comprises a nearest neighbor selection operation, and wherein only a selected subset of nearest neighbor data in the dataset is used to generate each corresponding model data.
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Abstract
A method for modelling a dataset includes a training phase, wherein the dataset is applied to a non-stationary Gaussian process kernel in order to optimize the values of a set of hyperparameters associated with the Gaussian process kernel, and an evaluation phase in which the dataset and Gaussian process kernel with optimized hyperparameters are used to generate model data. The evaluation phase includes a nearest neighbor selection step. The method may include generating a model at a selected resolution.
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Citations
20 Claims
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1. A method for modelling data based on a dataset, comprising:
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in a computing system, executing; a training phase, wherein the dataset is applied to a non-stationary Gaussian process kernel in order to optimize the values of a set of hyperparameters associated with the non-stationary Gaussian process kernel, and an evaluation phase in which the dataset and the non-stationary Gaussian process kernel with optimized hyperparameters are used to generate model data, wherein the evaluation phase comprises a nearest neighbor selection operation, and wherein only a selected subset of nearest neighbor data in the dataset is used to generate each corresponding model data. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A method for modelling a dataset with spatial characteristics, comprising
deriving, using a processing unit, a model from the dataset by providing the dataset to a Gaussian process using a non-stationary kernel, saving, within a memory, the dataset in a database that preserves the spatial characteristics of the dataset, wherein the database is adapted to provide a subset of nearest neighbour data in the dataset during regression of the Gaussian process.
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15. A system for modelling an area of terrain, comprising
computer hardware, computer software, and computer memory containing information for defining a model for the area of terrain, the computer software comprising instructions for: -
a modelling module arranged to receive measured terrain data and utilise a Gaussian process using a non-stationary kernel to derive a model for the area of terrain, and a data structure arranged to receive a dataset comprising at least a subset of the measured terrain data, wherein the data structure preserves spatial characteristics of the dataset and is adapted to provide a subset of nearest neighbour data in the dataset during regression of the Gaussian process. - View Dependent Claims (16, 17, 18, 19)
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20. A non-transitory computer readable medium storing executable instructions that when executed by a computer implement a method for modeling data based on a dataset, comprising:
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a training phase, wherein the dataset is applied to a non-stationary Gaussian process kernel in order to optimize the values of a set of hyperparameters associated with the non-stationary Gaussian process kernel, wherein the non-stationary Gaussian process kernel is a neural network kernel, and an evaluation phase in which the dataset and the non-stationary Gaussian process kernel with optimized hyperparameters are used to generate model data, wherein the evaluation phase comprises a nearest neighbour selection operation, wherein only a selected subset of nearest neighbour data in the dataset is used to generate each corresponding model data.
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Specification